Moinat Laure, Kasparian Jérôme, Brunetti Maura
Group of Applied Physics and Institute for Environmental Sciences, University of Geneva, 66 Bd Carl-Vogt, CH-1211 Geneva 4, Switzerland.
Chaos. 2024 Dec 1;34(12). doi: 10.1063/5.0230848.
The development of robust Early Warning Signals (EWSs) is necessary to quantify the risk of crossing tipping points in the present-day climate change. Classically, EWSs are statistical measures based on time series of climate state variables, without exploiting their spatial distribution. However, spatial information is crucial to identify the starting location of a transition process and can be directly inferred by satellite observations. By using complex networks constructed from several climate variables on the numerical grid of climate simulations, we seek for network properties that can serve as EWSs when approaching a state transition. We show that network indicators such as the normalized degree, the average length distance, and the betweenness centrality are capable of detecting tipping points at the global scale, as obtained by the MIT general circulation model in a coupled-aquaplanet configuration for CO2 concentration-driven simulations. The applicability of such indicators as EWSs is assessed and compared to traditional methods. We also analyze the ability of climate networks to identify nonlinear dynamical patterns.
开发强大的早期预警信号（EWS）对于量化当前气候变化中越过临界点的风险是必要的。传统上，EWS是基于气候状态变量的时间序列的统计量度，而没有利用其空间分布。然而，空间信息对于确定转变过程的起始位置至关重要，并且可以通过卫星观测直接推断出来。通过使用在气候模拟的数值网格上由几个气候变量构建的复杂网络，我们寻找在接近状态转变时可以用作EWS的网络属性。我们表明，诸如归一化度、平均长度距离和介数中心性等网络指标能够在全球尺度上检测临界点，这是由麻省理工学院通用循环模型在二氧化碳浓度驱动模拟的耦合水行星配置中获得的。评估了此类指标作为EWS的适用性，并与传统方法进行了比较。我们还分析了气候网络识别非线性动力学模式的能力。
